1. Field of the Invention
The present invention relates to a rotation based transformation method and apparatus that can perturb data using rotation for preserving data privacy, and more particularly, to a rotation based transformation method and apparatus that can mitigate the impact of the apriori knowledge independent component analysis (AK-ICA) attack on the rotation based transformation.
2. Description of the Related Art
A rotation based transformation (RBT) is effectively used for perturbing data used for data mining in order to maintain the individual data record's privacy. The advantage of the RBT is in its distance-invariant transformation. That is, the distance, inner product and angle between vectors of original numerical data is maintained even after the transformation is performed using the RBT. This is very important when, in data clustering based on a distance between data points, data transformed for the data clustering is used.
However, it has been discovered that the RBT may be vulnerable to the impact of the apriori knowledge independent component analysis (AK-ICA) attack. The independent component analysis (ICA) basically is a method for separating multivariate signals into additive components assuming the mutual statistical independence of non-Gaussian source signals. Moreover, the AK-ICA utilizes a portion of private data that an attacker knows to enhance the attack power. The power of the AK-ICA attack relies on several assumptions, but the most important thing is that a data sample given to the attacker has enough information in respect to the distribution of the entire data population, the data is of a specific type (according to the ICA conditions of work), and the attacker has enough information about the statistical properties of the data (for example, a minimum value and a maximum value).